The continuing desire and demand for cost-effective, sustainable, low-emissions power generation sources has in large part driven public and private sector interest in wind energy. According to the United States Department of Energy's 2008 20% Wind Energy by 2030 and March 2015 Wind Vision Report reports, the percentage of electricity generated by wind energy in the United States is projected to increase from approximately 4.5% in 2013 to nearly 20% by 2030 and 35% by 2050. In 2015, global investment in wind generation reached $110 billion, while in the United States alone investments reached $44 billion.
While increased wind power generation is generally considered desirable, the installation of the wind turbines used to generate this power (often co-located in “wind farms”) interfere with radar systems such as used for air traffic control and air defense missions. Current radar systems have a detection radius of up to 250 nautical miles and are affected by wind turbines within a 196,000 square mile detection area. The number, size, and height of the turbines as well as the blade rotation are known to impair/degrade the performance of radar systems in at least two ways: 1) by causing strong echoes from the turbine structure itself (thus possibly obscuring a target of interest); and, 2) as a result of Doppler frequency shifts due to the movement of the wind turbine blades with respect to the radar receiver (falsely suggesting that the wind turbine is moving in relation to the radar and thus is a possible target of interest).
The principle of operation of radar systems is generally straightforward, and may be understood with reference to FIG. 1, which illustrates a simplified version of an exemplary known pulse radar system. A signal generated and amplified at transmitter 110 is emitted from antenna 130 via switch (e.g., duplexer 120) in the form of continuous or pulsed radiofrequency waves that propagate outward from the antenna 130. Any object illuminated by the transmitted signal reflects some of this energy (generally referred to as “echoes” or “returns”) back to the radar's receiver 140 where it is digitized 150 and subsequently further processed for various information (e.g., “range” (or line of sight distance from radar to target) as well as azimuth (or bearing) and elevation (both determined by measuring the direction in which the antenna is pointed when the echo is received)). In pulse radar systems such as the Air Route Surveillance Radar (“ARSR”) used by the U.S. Air Force and the Federal Aviation Administration, energy is transmitted using a coherent train of modulated pulses and the return or echo signals are received in between the transmitted pulses.
In addition to range, azimuth, and bearing information, many radar systems exploit the Doppler effect by analyzing any frequency shift between the return signal(s) and the transmitted signal to ascertain how an object's motion has altered and thus to determine the radial velocity (or, “range-rate”) of the object(s) returning the echo(es) to the radar itself. This Doppler shift also allows a radar system to discriminate a moving target of interest from an object not of interest (i.e., “clutter”)
Early improvements for suppression of clutter in pulse radar systems included the adoption of moving target indication (MTI) that, through the use of delay-line cancelers, provided for the improved suppression of the display of echo signals from objects that were stationary or quasi-stationary. Pulse-Doppler or pulse radars were later devised to improve upon MTI radars and were designed to operate in severely cluttered environments conditions, where signals from targets of interest were received together with signals from many other objects in the environment.
Pulse-Doppler radar systems use Doppler frequency processing 164 to convert the time-domain reflected radar signals into the frequency domain, such that any Doppler shift appears as a change in phase of received signals between consecutive radar pulses. More specifically, returns from each transmitted pulse are received by an array of sensors, digitized, and then stored in memory “bins” according to range and time of arrival relative to the transmitted pulse. Each range-time bin is made up of a number of multidimensional range resolution cells, where a “range resolution cell” or “range cell” corresponds to a given distance of the radar and is generally understood to be the smallest range increment a particular radar system is capable of detecting. (The total number of range cells for a radar system may be determined by dividing the total range of the radar into its range resolution).
Subsequently, each set of sampled pulses for each range cell is then provided to a bank of narrow-band digital filters (in processing block 164) where they are preferably Fourier-transformed. The magnitude of each output of each spectral band is determined and the outputs subsequently stored in Doppler frequency bins. More specifically, each of the Doppler filters in the bank has a predetermined separate threshold that is used for determining the Doppler, or radial, velocity of the target that is the subject of the returned signal. This threshold may be hard-coded in software, burned into firmware or hardwired. If the amplitude out of the output of the returned signals from targets having zero velocity (i.e., are stationary) are placed into one memory bin, while the amplitude of those returned signals from targets that have a non-zero velocity (i.e., are moving) are input into non-zero Doppler frequency bins. The signal in a particular range-Doppler frequency bin thus corresponds to a signal from an object, or a portion of the illuminated area, at that range and moving with a particular speed. This signal processing thus allows the separation of moving targets from slow-moving clutter. The signal filter processing techniques as discussed above have been used in later pulse radar systems such as later generations of MTI radar systems (e.g., pulsed coherent MTI) as well as in MTD radar systems. (As known in the art, MTD radar systems generally combine Doppler filter banks adapted to process groups of pulses with a fine grained clutter map that is used to establish thresholds for zero radial velocity cells).
After these signal processing steps, detection and thresholding generally involves applying a detection process such as constant false alarm rate (CFAR) (included in block 172). At its basic level, a detection process such as CFAR involves comparing the signal power level of a particular range cell under test (CUT) with local average signal power level. This local average signal power level may be determined by calculating an average over adjacent ranges but within the same Doppler frequency bin, or over adjacent Doppler frequency bins. If the ratio of the signal power level for the CUT to the local average signal power level exceeds a predetermined or selected threshold, a target is declared and may be indicated on the radar display dependent on further processing. One challenge arising from existing detection systems is the difficulty in adjusting a threshold so as to provide a balance between providing adequate sensitivity in identifying a target while still eliminating sufficient clutter.
While the foregoing and similar techniques have proven reasonable for distinguishing targets of interest from stationary, quasi-stationary, and objects having an expected velocity profile, they have been less successful in addressing the challenges presented by wind turbines because of their size and their complex Doppler frequency profile. More specifically, with regard to the first technical challenge, wind turbines present a very large radar signature or radar cross section (RCS) compared with intended targets. The cumulative effect of the turbine tower and the blades can present a radar echo larger than that of a Boeing 747. Due to the excessively large echo received from the turbine, the radar system can mistakenly identify the turbine as a target of interest and overlook real airborne targets in front of, behind, or over the top of the turbine. Thus the clutter created by wind turbines can result in a virtual complete loss of radar detection of real targets above, in, and around wind farms.
Furthermore, as known in the art, the blades of the wind turbine can present velocities that range from 0 (at the hub) to more than 200 mph (tip of the blade). It will thus be appreciated that this design leads to the second technical challenge: wind turbines produce dramatic outlier readings for amplitude and velocity and readings that fluctuate significantly over periods of time. The fluctuation results from the large permutation of positional variations of the wind turbine blades when the turbine is being illuminated by the radar system. Accordingly, each time a radar signal comes into contact with a blade at a different position, the system perceives a change in amplitude and velocity in the return signal, thus signifying a retreating or advancing object that may lead to an erroneous detection of a target. A resulting false alarm may thus be triggered. This compounds the previously-identified existing challenge for detection techniques.
Other complex structures (e.g., vehicles traveling along a road) present similar challenges in detection techniques.
As a result of the foregoing, and despite their desirability, wind turbines and wind farms and other complex structures severely impair the ability of radar systems to discriminate between wanted and unwanted targets and, as a result, potentially compromise airspace safety and national security interests. Therefore, it is desired to provide in such environments techniques to improve detection of target(s) in the vicinity of cluttered environments such as wind farms and to reduce false alarms resulting from wind turbines and other complex structures.